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dc.contributor.authorDodson, C.T.J.pt_BR
dc.contributor.authorScharcanski, Jacobpt_BR
dc.date.accessioned2011-01-29T06:00:26Zpt_BR
dc.date.issued2003pt_BR
dc.identifier.issn1083-4427pt_BR
dc.identifier.urihttp://hdl.handle.net/10183/27592pt_BR
dc.description.abstractWe outline the information-theoretic differential geometry of gamma distributions, which contain exponential distributions as a special case, and log-gamma distributions. Our arguments support the opinion that these distributions have a natural role in representing departures from randomness, uniformity, and Gaussian behavior in stochastic processes. We show also how the information geometry provides a surprisingly tractable Riemannian manifold and product spaces thereof, on which may be represented the evolution of a stochastic process, or the comparison of different processes, by means of well-founded maximum likelihood parameter estimation. Our model incorporates possible correlations among parameters. We discuss applications and provide some illustrations from a recent study of amino acid self-clustering in protein sequences; we provide also some results from simulations for multisymbol sequences.en
dc.format.mimetypeapplication/pdfpt_BR
dc.language.isoengpt_BR
dc.relation.ispartofIEEE transactions on systems, man, and cybernetics. A, Systems and humans. New York. Vol. 33, No. 4 (2003), p. 435-440pt_BR
dc.rightsOpen Accessen
dc.subjectGamma modelsen
dc.subjectMatemáticapt_BR
dc.subjectInformation geometryen
dc.subjectMultisymbol sequencesen
dc.subjectRandomen
dc.subjectSearchen
dc.subjectStochastic processen
dc.titleInformation geometric similarity measurement for near-random stochastic processespt_BR
dc.typeArtigo de periódicopt_BR
dc.identifier.nrb000392547pt_BR
dc.type.originEstrangeiropt_BR


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